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 # HydroDLAdj
-This code solves ODEs of hydrological models with adjoint.
+This code is for an adjoint-based differentiable model to enable implicit ODE solutions for large-scale hydrological modeling, reducing the distortion of fluxes and physical parameters caused by numerical errors in previous models that used explicit and operation-splitting schemes. Please follow the provided examples to use this code. If you find this code useful for your research, please cite the papers listed below.
+
+Song, Yalan, Wouter JM Knoben, Martyn P. Clark, Dapeng Feng, Kathryn E. Lawson, and Chaopeng Shen. "When ancient numerical demons meet physics-informed machine learning: adjoint-based gradients for implicit differentiable modeling." Hydrology and Earth System Sciences Discussions 2023 (2023): 1-35.
+
+Feng, D., Liu, J., Lawson, K., & Shen, C. (2022). Differentiable, learnable, regionalized process-based models with multiphysical outputs can approach state-of-the-art hydrologic prediction accuracy. Water Resources Research, 58, e2022WR032404. https://doi.org/10.1029/2022WR032404